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概要と使いどころ
Use when analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.
日本語の概要は準備中です。原文の説明を表示しています。
Perform critical Rust code reviews covering correctness, edition 2024 compliance, error handling, API design, async pitfalls, and dependency hygiene. ALWAYS use this skill when the user wants to review, audit, or critically evaluate Rust code — whether that's a PR diff, a specific crate or module, a cross-cutting concern like error handling, or the whole codebase. This includes requests to "review my changes", "check this code", "audit for best practices", "look for issues", "what's wrong with this", or "flag anything that could bite us". Use it even when the user doesn't say "review" explicitly but is asking you to find problems, inconsistencies, or anti-patterns in existing Rust code. Do NOT use for requests to write, fix, refactor, test, explain, or implement code — only for evaluating existing code.
日本語の概要は準備中です。原文の説明を表示しています。
Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.
日本語の概要は準備中です。原文の説明を表示しています。
Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.
日本語の概要は準備中です。原文の説明を表示しています。
Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
日本語の概要は準備中です。原文の説明を表示しています。
Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a CONTEXT.md, or recording or editing an ADR.
日本語の概要は準備中です。原文の説明を表示しています。
Use when needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.
日本語の概要は準備中です。原文の説明を表示しています。
Create a PRD through user interview, codebase exploration, and module design, then submit as a GitHub issue. Use when user wants to write a PRD, create a product requirements document, or plan a new feature.
日本語の概要は準備中です。原文の説明を表示しています。
Create bulletproof Terms of Service and Privacy Policy documents for SaaS applications. Infers company information from codebase/marketing site, conducts comprehensive audits, drafts documents, then asks user ONLY for missing details at the end. Minimizes user interaction. Use when the user needs to draft, review, or update legal documents (ToS, Terms of Service, Privacy Policy, legal pages). Triggers on requests for legal documents, terms drafting, privacy policy creation, "cover our bases legally", liability protection, or legal compliance for software products.
日本語の概要は準備中です。原文の説明を表示しています。
Discover how a codebase already handles a specific concern — search broadly, find every instance, and assess consistency. The "how does this app do X?" tool.
日本語の概要は準備中です。原文の説明を表示しています。
Validate Rust code quality with rustfmt, clippy, cargo check, and security analysis. Use for Rust codebases to ensure idiomatic Rust code and best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Validate Python code quality with formatting, type checking, linting, and security analysis. Use for Python codebases to ensure PEP 8 compliance, type safety, and code quality.
日本語の概要は準備中です。原文の説明を表示しています。
Scan codebase for security vulnerabilities including secrets, insecure dependencies, and unsafe code patterns. Use when performing automated security scans.
日本語の概要は準備中です。原文の説明を表示しています。
Validate TypeScript/JavaScript code quality with ESLint, Prettier, type checking, and security analysis. Use for TypeScript/JS codebases to ensure code quality and standards.
日本語の概要は準備中です。原文の説明を表示しています。
Detect technical debt — duplicated code, dead code, TODOs, and oversized functions across the codebase. Use at the end of a session or when auditing code hygiene. Trigger on "find tech debt", "any dead code", "duplicated code", "code hygiene", "techdebt".
日本語の概要は準備中です。原文の説明を表示しています。
Write a durable feature spec before touching code. Reads the codebase map, reads related models/migrations to ask technical questions (not generic ones), then produces a structured spec doc in docs/specs/. Designed to pipeline with /implementation --issue N. Use before any feature implementation.
日本語の概要は準備中です。原文の説明を表示しています。
30 → 5 → 15 funnel for brainstorming new features on the existing Forge codebase. Use when adding capability to a mature install, not for greenfield.
日本語の概要は準備中です。原文の説明を表示しています。
Rapidly map a new codebase, identify the runtime model, risk zones, and first high-value next steps.
日本語の概要は準備中です。原文の説明を表示しています。
Generates specific, implementable fixes for every finding in findings.json. Produces code patches (unified diff), configuration changes, dependency updates, and IaC fixes — not generic advice but actual before/after code. Uses the reproduction command from each finding as the verification step: "run this after the fix — it should now fail." Stores remediation data in findings.json so the dashboard shows a Fix button and /gh-export includes the remediation in GitHub issues. Chains from /pentester, /codebase, or any scan skill after findings are produced. Chains into /gh-export for export with remediation included.
日本語の概要は準備中です。原文の説明を表示しています。
Full penetration test using MCP tools — recon, scanning, exploitation, and reporting. Tailored for OpenCode (BYO LLM). Supports network/web targets and local codebases. Chains into analyze-cve, threat-modeling, and remediate skills automatically.
日本語の概要は準備中です。原文の説明を表示しています。
White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.
日本語の概要は準備中です。原文の説明を表示しています。
Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.
日本語の概要は準備中です。原文の説明を表示しています。
Triages an Aikido security findings CSV against a local codebase. For each finding, reads the flagged file, traces the code path, and verdicts it as KEEP OPEN or CLOSE with a specific reason. Outputs a reviewed CSV and a self-contained HTML evidence report. Run this at the end of a pentest when an Aikido CSV is available.
日本語の概要は準備中です。原文の説明を表示しています。
Deep API security assessment beyond surface scanning. Covers the full OWASP API Security Top 10 (2023): Broken Object Level Authorization (BOLA / IDOR), Broken Authentication, Broken Object Property Level Authorization (mass assignment + excessive data exposure), Unrestricted Resource Consumption, Broken Function Level Authorization (BFLA / vertical privilege escalation), Unrestricted Access to Sensitive Business Flows, Server-Side Request Forgery via API parameters, Security Misconfiguration, Improper Inventory Management (shadow/zombie/deprecated endpoints, v1/v2 drift), and Unsafe Consumption of third-party APIs. Works across REST, GraphQL, gRPC, SOAP, and MCP servers. Discovers APIs from OpenAPI/Swagger specs, GraphQL introspection, gRPC reflection, .well-known endpoints, JS bundles, and traffic capture. Uses kiterunner, ffuf, schemathesis, restler-fuzzer, openapi-fuzzer, graphql-cop, clairvoyance, batchql, inql, jwt_tool, postman, mitmproxy, and manual http(action="request", ...) payloads. Every technique includes actual payloads, commands, and verification logic. Chains from /pentester or /codebase when API endpoints are discovered, chains into /web-exploit when classic injection points are found in API parameters, chains into /post-exploit when RCE is achieved, and chains into /ai-redteam when an LLM/AI endpoint is discovered (chat APIs, completion endpoints, RAG search, agentic tool-use, MCP servers).
日本語の概要は準備中です。原文の説明を表示しています。